1. Field of the Invention
The present invention relates to an image processing technique for converting a given known image into an unknown image with high accuracy.
2. Description of the Related Art
As an image processing method for converting a known image into an unknown image, Jianchao Yang, Zhaowen Wang, Zhe Lin, Scott Cohen and Thomas Huang, “Couple Dictionary Training for Image Super-Resolution”, Transactions on Image Processing, U.S.A., IEEE, 2012, Vol. 21, Issue8, p. 3467-3478 (hereinafter referred to as “Document 1”) discloses so-called super-resolution processing to produce, from a low-resolution image produced through a degradation process such as pixel decimation from a high-resolution image, the high-resolution image on which the degradation process is not performed. Specifically, the super-resolution processing first performs an interpolation process such as a nearest neighbor method on the low-resolution image to produce a high-resolution intermediate image. The intermediate image is smoothed due to the interpolation process, so that the super-resolution processing next arbitrarily extracts a small area (patch) from the intermediate image and converts the patch into an unsmoothed corresponding patch of a high-resolution image to be produced. Performing such processes on the entire intermediate image enables producing the high-resolution image (super-resolution image).
On the other hand, Michael Elad and Michal Aharon, “Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries”, Transactions on Image Processing, U.S.A, IEEE, 2006, Vol. 15, Issue 12, p. 3736-3745 (hereinafter referred to as “Document 2”) discloses a so-called noise removal process to acquire, from a degraded image containing noise, an original image not containing the noise. Specifically, the noise removal process first converts a patch arbitrarily extracted from the degraded image into a corresponding patch not containing the noise in the original image to be produced. Performing such a process on the entire degraded image produces the original image with the noise removed.
As just described above, conventional image processing methods perform a process to convert the patch extracted from the degraded image into the corresponding patch of the original image not having been degraded and thereby produce the high-resolution image or the noise-removed image.
The image processing methods disclosed in Documents 1 and 2 use a dictionary beforehand produced by dictionary learning (or dictionary training) from multiple patches extracted from a training image not having been degraded or from a set of training images having been and not being degraded. Such image processing methods are each called an image processing method based on sparse expression or sparse coding used in the following description. The dictionary means a set of elements that are multiple patches produced as a result of the dictionary learning. The training image means an image to be used for producing the dictionary by the dictionary learning.
However, the image processing methods disclosed in Documents 1 and 2 cannot convert an arbitrary known image (hereinafter referred to as “a first image”) into an arbitrary unknown image (hereinafter referred to as “a second image”) with high accuracy. The image processing method disclosed in Document 2 can convert the patch extracted from the degraded image into the corresponding patch in the original image and, however, cannot in principle convert the arbitrary first image into the arbitrary second image. In addition, the image processing method disclosed in Document 1 can merely convert the arbitrary first image into the arbitrary second image and, however, cannot perform the conversion with high accuracy.